Comparison
agentos vs awesome
Verdict
Pick agentos when license: agentos is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, agentos is Apache-2.0.
Markdown twin · agentos alternatives · awesome alternatives
GraphCanon updated today
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Trust & integrity
| Signal | agentos | awesome |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- agentos
- TypeScript AI agent framework: cognitive memory, runtime tool forging, multi-agent orchestration, 11 LLM providers.
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- agentos
- 591
- awesome
- 484k
Forks
- agentos
- 88
- awesome
- 36k
Open issues
- agentos
- 6
- awesome
- 92
Language
- agentos
- TypeScript
- awesome
- -
Adopt for
- agentos
- -
- awesome
- -
Persona
- agentos
- -
- awesome
- -
Runtime
- agentos
- -
- awesome
- -
License
- agentos
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- agentos
- Jul 11, 2026
- awesome
- Jun 30, 2026
Categories
- agentos
- AI Agents, LLM Frameworks, Vector Databases
- awesome
- LLM Frameworks
Trust and health
Maintenance
- agentos
- Very active (96%)
- awesome
- Active (82%)
Days since push
- agentos
- 0d
- awesome
- 11d
Open issues (now)
- agentos
- 6
- awesome
- 92
Owner type
- agentos
- Organization
- awesome
- User
Full report
- agentos
- Trust report
- awesome
- Trust report
Choose agentos if…
- License: agentos is Apache-2.0, awesome is CC0-1.0.
- Tags unique to agentos: agent-framework, agent-memory, agentic-ai, ai-agent-framework.
- Also covers AI Agents, Vector Databases.
When NOT to use agentos
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose awesome if…
- License: awesome is CC0-1.0, agentos is Apache-2.0.
- Tags unique to awesome: awesome-list, resources.
- More GitHub stars (484k vs 591) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (framerslab/agentos) · observed Jul 11, 2026
- GitHub forks (framerslab/agentos) · observed Jul 11, 2026
- Last push (framerslab/agentos) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentos 591 · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between agentos and awesome?
- agentos: TypeScript AI agent framework: cognitive memory, runtime tool forging, multi-agent orchestration, 11 LLM providers.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentos over awesome?
- Choose agentos over awesome when License: agentos is Apache-2.0, awesome is CC0-1.0; Tags unique to agentos: agent-framework, agent-memory, agentic-ai, ai-agent-framework; Also covers AI Agents, Vector Databases.
- When should I choose awesome over agentos?
- Choose awesome over agentos when License: awesome is CC0-1.0, agentos is Apache-2.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 591) - visibility, not fit.
- When should I avoid agentos?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is agentos or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 591). Stars measure visibility, not whether either tool fits your constraints.
- Are agentos and awesome open source?
- Yes - both are open-source projects on GitHub (agentos: Apache-2.0, awesome: CC0-1.0).
- Where can I find alternatives to agentos or awesome?
- GraphCanon lists graph-backed alternatives at agentos alternatives and awesome alternatives (agentos markdown twin, awesome markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, agentos or awesome?
- agentos: Very active. awesome: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for agentos and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentos trust report; awesome trust report.